Scalable group level probabilistic sparse factor analysis

Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a scalable group level probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component...

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Bibliographic Details
Main Authors: Hinrich, Jesper L., Nielsen, Soren F. V., Riis, Nicolai A. B., Eriksen, Casper T., Frosig, Jacob, Kristensen, Marco D. F., Schmidt, Mikkel N., Madsen, Kristoffer H., Morup, Morten
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Correlation
Data models
Graphics processing units
independent components
Indexes
neuroimaging
Principal component analysis
probabilistic factor analysis
Probabilistic logic
sparsity
variational Bayes
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Summary:Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a scalable group level probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise modeling. For task-based and resting state fMRI, we show that the sparsity constraint gives rise to components similar to those obtained by group independent component analysis. The noise modeling shows that noise is reduced in areas typically associated with activation by the experimental design. The psFA model identifies sparse components and the probabilistic setting provides a natural way to handle parameter uncertainties. The variational Bayesian framework easily extends to more complex noise models than the presently considered.
ISSN:2379-190X
DOI:10.1109/ICASSP.2017.7953371